Overview

Dataset statistics

Number of variables14
Number of observations569
Missing cells887
Missing cells (%)11.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.0 KiB
Average record size in memory124.2 B

Variable types

Numeric6
Categorical6
Text2

Dataset

Description경상남도 도로대장전산화 시스템 데이터의 중장기개방계획에 따른 데이터입니다. 시스템 상에서의 각 도로별 시설물 기본정보를 가지고 있으며, 도로대장의 표지 데이터를 포함하고있습니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15091917

Alerts

관리기관 has constant value ""Constant
도로종류 has constant value ""Constant
노선번호 has constant value ""Constant
이력코드 has constant value ""Constant
식별번호 is highly overall correlated with 관리번호 and 1 other fieldsHigh correlation
관리번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
위치 is highly overall correlated with 관리번호High correlation
설치형식 is highly overall correlated with 구간번호High correlation
구간번호 is highly overall correlated with 식별번호 and 2 other fieldsHigh correlation
설치형식 has 410 (72.1%) missing valuesMissing
사진 has 477 (83.8%) missing valuesMissing
식별번호 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:20:11.651480
Analysis finished2023-12-11 00:20:16.019493
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식별번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct569
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285
Minimum1
Maximum569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T09:20:16.092151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.4
Q1143
median285
Q3427
95-th percentile540.6
Maximum569
Range568
Interquartile range (IQR)284

Descriptive statistics

Standard deviation164.40043
Coefficient of variation (CV)0.5768436
Kurtosis-1.2
Mean285
Median Absolute Deviation (MAD)142
Skewness0
Sum162165
Variance27027.5
MonotonicityStrictly increasing
2023-12-11T09:20:16.232304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
383 1
 
0.2%
377 1
 
0.2%
378 1
 
0.2%
379 1
 
0.2%
380 1
 
0.2%
381 1
 
0.2%
382 1
 
0.2%
384 1
 
0.2%
375 1
 
0.2%
Other values (559) 559
98.2%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
569 1
0.2%
568 1
0.2%
567 1
0.2%
566 1
0.2%
565 1
0.2%
564 1
0.2%
563 1
0.2%
562 1
0.2%
561 1
0.2%
560 1
0.2%

관리번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct569
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10200285
Minimum10200001
Maximum10200569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T09:20:16.369190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10200001
5-th percentile10200029
Q110200143
median10200285
Q310200427
95-th percentile10200541
Maximum10200569
Range568
Interquartile range (IQR)284

Descriptive statistics

Standard deviation164.40043
Coefficient of variation (CV)1.6117238 × 10-5
Kurtosis-1.2
Mean10200285
Median Absolute Deviation (MAD)142
Skewness0
Sum5.8039622 × 109
Variance27027.5
MonotonicityNot monotonic
2023-12-11T09:20:16.530493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10200134 1
 
0.2%
10200148 1
 
0.2%
10200142 1
 
0.2%
10200143 1
 
0.2%
10200144 1
 
0.2%
10200147 1
 
0.2%
10200145 1
 
0.2%
10200146 1
 
0.2%
10200149 1
 
0.2%
10200139 1
 
0.2%
Other values (559) 559
98.2%
ValueCountFrequency (%)
10200001 1
0.2%
10200002 1
0.2%
10200003 1
0.2%
10200004 1
0.2%
10200005 1
0.2%
10200006 1
0.2%
10200007 1
0.2%
10200008 1
0.2%
10200009 1
0.2%
10200010 1
0.2%
ValueCountFrequency (%)
10200569 1
0.2%
10200568 1
0.2%
10200567 1
0.2%
10200566 1
0.2%
10200565 1
0.2%
10200564 1
0.2%
10200563 1
0.2%
10200562 1
0.2%
10200561 1
0.2%
10200560 1
0.2%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1683
569 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1683
2nd row1683
3rd row1683
4th row1683
5th row1683

Common Values

ValueCountFrequency (%)
1683 569
100.0%

Length

2023-12-11T09:20:16.661515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:16.748687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1683 569
100.0%

도로종류
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1504
569 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1504
2nd row1504
3rd row1504
4th row1504
5th row1504

Common Values

ValueCountFrequency (%)
1504 569
100.0%

Length

2023-12-11T09:20:16.850991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:16.935972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1504 569
100.0%

노선번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1020
569 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1020
2nd row1020
3rd row1020
4th row1020
5th row1020

Common Values

ValueCountFrequency (%)
1020 569
100.0%

Length

2023-12-11T09:20:17.012574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:17.092309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1020 569
100.0%

구간번호
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
3
275 
4
159 
1
135 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
3 275
48.3%
4 159
27.9%
1 135
23.7%

Length

2023-12-11T09:20:17.187850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:17.267817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 275
48.3%
4 159
27.9%
1 135
23.7%

이력코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
0
569 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 569
100.0%

Length

2023-12-11T09:20:17.353652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:17.431928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 569
100.0%

위치
Real number (ℝ)

HIGH CORRELATION 

Distinct465
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2705747
Minimum0.008
Maximum11.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T09:20:17.520789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.228
Q11.251
median3.064
Q37.226
95-th percentile10.3006
Maximum11.46
Range11.452
Interquartile range (IQR)5.975

Descriptive statistics

Standard deviation3.3830734
Coefficient of variation (CV)0.79218224
Kurtosis-1.128537
Mean4.2705747
Median Absolute Deviation (MAD)2.501
Skewness0.48291443
Sum2429.957
Variance11.445186
MonotonicityNot monotonic
2023-12-11T09:20:17.677544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.842 5
 
0.9%
0.073 5
 
0.9%
2.851 4
 
0.7%
0.321 4
 
0.7%
0.286 3
 
0.5%
1.829 3
 
0.5%
2.132 3
 
0.5%
2.9 3
 
0.5%
0.48 3
 
0.5%
0.228 3
 
0.5%
Other values (455) 533
93.7%
ValueCountFrequency (%)
0.008 2
 
0.4%
0.009 1
 
0.2%
0.021 1
 
0.2%
0.022 2
 
0.4%
0.023 2
 
0.4%
0.028 1
 
0.2%
0.037 1
 
0.2%
0.043 1
 
0.2%
0.046 1
 
0.2%
0.073 5
0.9%
ValueCountFrequency (%)
11.46 1
0.2%
11.429 1
0.2%
11.33 2
0.4%
11.313 1
0.2%
11.226 1
0.2%
11.224 2
0.4%
11.22 1
0.2%
11.191 1
0.2%
11.18 1
0.2%
11.151 1
0.2%

위치_방향
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
1
296 
0
273 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 296
52.0%
0 273
48.0%

Length

2023-12-11T09:20:17.792009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:17.874992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 296
52.0%
0 273
48.0%

표지종류
Real number (ℝ)

Distinct11
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1813.6098
Minimum1801
Maximum1899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T09:20:17.961094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1801
5-th percentile1803
Q11805
median1807
Q31807
95-th percentile1899
Maximum1899
Range98
Interquartile range (IQR)2

Descriptive statistics

Standard deviation25.697022
Coefficient of variation (CV)0.014168991
Kurtosis7.1901661
Mean1813.6098
Median Absolute Deviation (MAD)1
Skewness3.0183475
Sum1031944
Variance660.33695
MonotonicityNot monotonic
2023-12-11T09:20:18.051797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1807 153
26.9%
1805 116
20.4%
1808 90
15.8%
1806 77
13.5%
1803 68
12.0%
1899 47
 
8.3%
1802 7
 
1.2%
1801 5
 
0.9%
1804 4
 
0.7%
1809 1
 
0.2%
ValueCountFrequency (%)
1801 5
 
0.9%
1802 7
 
1.2%
1803 68
12.0%
1804 4
 
0.7%
1805 116
20.4%
1806 77
13.5%
1807 153
26.9%
1808 90
15.8%
1809 1
 
0.2%
1810 1
 
0.2%
ValueCountFrequency (%)
1899 47
 
8.3%
1810 1
 
0.2%
1809 1
 
0.2%
1808 90
15.8%
1807 153
26.9%
1806 77
13.5%
1805 116
20.4%
1804 4
 
0.7%
1803 68
12.0%
1802 7
 
1.2%

표지명칭
Real number (ℝ)

Distinct22
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4073.6186
Minimum4001
Maximum4099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T09:20:18.162279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4001
5-th percentile4008
Q14088
median4088
Q34088
95-th percentile4088
Maximum4099
Range98
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.784208
Coefficient of variation (CV)0.0073114865
Kurtosis0.51518139
Mean4073.6186
Median Absolute Deviation (MAD)0
Skewness-1.5384054
Sum2317889
Variance887.09902
MonotonicityNot monotonic
2023-12-11T09:20:18.298444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4088 434
76.3%
4029 27
 
4.7%
4099 19
 
3.3%
4010 17
 
3.0%
4007 14
 
2.5%
4009 11
 
1.9%
4008 8
 
1.4%
4012 7
 
1.2%
4004 5
 
0.9%
4039 5
 
0.9%
Other values (12) 22
 
3.9%
ValueCountFrequency (%)
4001 1
 
0.2%
4003 4
 
0.7%
4004 5
 
0.9%
4007 14
2.5%
4008 8
1.4%
4009 11
1.9%
4010 17
3.0%
4011 2
 
0.4%
4012 7
1.2%
4013 1
 
0.2%
ValueCountFrequency (%)
4099 19
 
3.3%
4088 434
76.3%
4039 5
 
0.9%
4030 1
 
0.2%
4029 27
 
4.7%
4024 1
 
0.2%
4022 1
 
0.2%
4020 2
 
0.4%
4018 2
 
0.4%
4017 1
 
0.2%

설치형식
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)4.4%
Missing410
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1406.4088
Minimum1401
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.1 KiB
2023-12-11T09:20:18.430582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1401
5-th percentile1401
Q11401
median1403
Q31406
95-th percentile1409
Maximum1499
Range98
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.969524
Coefficient of variation (CV)0.012065854
Kurtosis26.081746
Mean1406.4088
Median Absolute Deviation (MAD)2
Skewness5.1899274
Sum223619
Variance287.96473
MonotonicityNot monotonic
2023-12-11T09:20:18.542770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1401 68
 
12.0%
1408 27
 
4.7%
1404 23
 
4.0%
1403 20
 
3.5%
1402 8
 
1.4%
1409 8
 
1.4%
1499 5
 
0.9%
(Missing) 410
72.1%
ValueCountFrequency (%)
1401 68
12.0%
1402 8
 
1.4%
1403 20
 
3.5%
1404 23
 
4.0%
1408 27
 
4.7%
1409 8
 
1.4%
1499 5
 
0.9%
ValueCountFrequency (%)
1499 5
 
0.9%
1409 8
 
1.4%
1408 27
 
4.7%
1404 23
 
4.0%
1403 20
 
3.5%
1402 8
 
1.4%
1401 68
12.0%

사진
Text

MISSING 

Distinct85
Distinct (%)92.4%
Missing477
Missing (%)83.8%
Memory size4.6 KiB
2023-12-11T09:20:18.776945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters1196
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)85.9%

Sample

1st row102004M08989U
2nd row102004M09091U
3rd row102004M08875D
4th row102004M08986D
5th row102004M09035D
ValueCountFrequency (%)
102004m00286d 3
 
3.3%
102004m08563u 2
 
2.2%
102004m08578d 2
 
2.2%
102004m00538d 2
 
2.2%
102004m00526u 2
 
2.2%
102004m09359u 2
 
2.2%
102004m00028u 1
 
1.1%
102004m01846u 1
 
1.1%
102004m00219u 1
 
1.1%
102004m00212u 1
 
1.1%
Other values (75) 75
81.5%
2023-12-11T09:20:19.135683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 419
35.0%
2 131
 
11.0%
1 119
 
9.9%
4 111
 
9.3%
M 92
 
7.7%
8 65
 
5.4%
D 47
 
3.9%
U 45
 
3.8%
5 41
 
3.4%
7 38
 
3.2%
Other values (3) 88
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1012
84.6%
Uppercase Letter 184
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 419
41.4%
2 131
 
12.9%
1 119
 
11.8%
4 111
 
11.0%
8 65
 
6.4%
5 41
 
4.1%
7 38
 
3.8%
6 32
 
3.2%
9 29
 
2.9%
3 27
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
M 92
50.0%
D 47
25.5%
U 45
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1012
84.6%
Latin 184
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 419
41.4%
2 131
 
12.9%
1 119
 
11.8%
4 111
 
11.0%
8 65
 
6.4%
5 41
 
4.1%
7 38
 
3.8%
6 32
 
3.2%
9 29
 
2.9%
3 27
 
2.7%
Latin
ValueCountFrequency (%)
M 92
50.0%
D 47
25.5%
U 45
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 419
35.0%
2 131
 
11.0%
1 119
 
9.9%
4 111
 
9.3%
M 92
 
7.7%
8 65
 
5.4%
D 47
 
3.9%
U 45
 
3.8%
5 41
 
3.4%
7 38
 
3.2%
Other values (3) 88
 
7.4%

비고
Text

Distinct244
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
2023-12-11T09:20:19.418110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length34
Mean length13.293497
Min length2

Characters and Unicode

Total characters7564
Distinct characters250
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)30.2%

Sample

1st row70km/h최고속도제한_(0)
2nd row유턴_(좌,보행신호시)
3rd row자전거횡단도_(0)
4th row좌측면통행_(0)
5th row자전거횡단도_(0)
ValueCountFrequency (%)
보조표지 71
 
9.3%
70km/h최고속도제한_(0 31
 
4.1%
견인지역_(0 19
 
2.5%
정차.주차금지_(0 18
 
2.4%
자전거전용도로_(0 16
 
2.1%
자전거횡단도_(0 15
 
2.0%
횡단보도 15
 
2.0%
80km/h최고속도제한_(0 12
 
1.6%
유턴_(0 12
 
1.6%
최고속도제한(60 11
 
1.4%
Other values (294) 542
71.1%
2023-12-11T09:20:19.916782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 566
 
7.5%
) 565
 
7.5%
_ 410
 
5.4%
0 398
 
5.3%
지 328
 
4.3%
도 266
 
3.5%
표 254
 
3.4%
194
 
2.6%
보 142
 
1.9%
고 121
 
1.6%
Other values (240) 4320
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4699
62.1%
Decimal Number 682
 
9.0%
Open Punctuation 566
 
7.5%
Close Punctuation 565
 
7.5%
Connector Punctuation 410
 
5.4%
Other Punctuation 206
 
2.7%
Space Separator 194
 
2.6%
Lowercase Letter 171
 
2.3%
Uppercase Letter 67
 
0.9%
Math Symbol 4
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 328
 
7.0%
도 266
 
5.7%
표 254
 
5.4%
보 142
 
3.0%
고 121
 
2.6%
차 118
 
2.5%
제 108
 
2.3%
방 104
 
2.2%
속 104
 
2.2%
로 101
 
2.1%
Other values (209) 3053
65.0%
Decimal Number
ValueCountFrequency (%)
0 398
58.4%
2 70
 
10.3%
3 56
 
8.2%
7 34
 
5.0%
1 32
 
4.7%
5 30
 
4.4%
6 27
 
4.0%
8 18
 
2.6%
4 13
 
1.9%
9 4
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
M 17
25.4%
A 12
17.9%
C 12
17.9%
E 12
17.9%
I 10
14.9%
K 4
 
6.0%
Lowercase Letter
ValueCountFrequency (%)
m 72
42.1%
k 49
28.7%
h 46
26.9%
c 2
 
1.2%
i 2
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 100
48.5%
/ 56
27.2%
. 43
20.9%
% 7
 
3.4%
Math Symbol
ValueCountFrequency (%)
~ 3
75.0%
+ 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 566
100.0%
Close Punctuation
ValueCountFrequency (%)
) 565
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 410
100.0%
Space Separator
ValueCountFrequency (%)
194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4699
62.1%
Common 2627
34.7%
Latin 238
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 328
 
7.0%
도 266
 
5.7%
표 254
 
5.4%
보 142
 
3.0%
고 121
 
2.6%
차 118
 
2.5%
제 108
 
2.3%
방 104
 
2.2%
속 104
 
2.2%
로 101
 
2.1%
Other values (209) 3053
65.0%
Common
ValueCountFrequency (%)
( 566
21.5%
) 565
21.5%
_ 410
15.6%
0 398
15.2%
194
 
7.4%
, 100
 
3.8%
2 70
 
2.7%
3 56
 
2.1%
/ 56
 
2.1%
. 43
 
1.6%
Other values (10) 169
 
6.4%
Latin
ValueCountFrequency (%)
m 72
30.3%
k 49
20.6%
h 46
19.3%
M 17
 
7.1%
A 12
 
5.0%
C 12
 
5.0%
E 12
 
5.0%
I 10
 
4.2%
K 4
 
1.7%
c 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4697
62.1%
ASCII 2865
37.9%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 566
19.8%
) 565
19.7%
_ 410
14.3%
0 398
13.9%
194
 
6.8%
, 100
 
3.5%
m 72
 
2.5%
2 70
 
2.4%
3 56
 
2.0%
/ 56
 
2.0%
Other values (21) 378
13.2%
Hangul
ValueCountFrequency (%)
지 328
 
7.0%
도 266
 
5.7%
표 254
 
5.4%
보 142
 
3.0%
고 121
 
2.6%
차 118
 
2.5%
제 108
 
2.3%
방 104
 
2.2%
속 104
 
2.2%
로 101
 
2.2%
Other values (208) 3051
65.0%
Compat Jamo
ValueCountFrequency (%)
ㅏ 2
100.0%

Interactions

2023-12-11T09:20:14.976501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.101543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.563909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.050282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.538413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.446957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:15.093042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.176549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.644849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.126029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.621514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.533412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:15.202549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.255315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.717985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.225953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.728343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.619864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:15.300023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.334915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.792806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.307456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.879494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.711217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:15.393512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.411319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.875786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.387548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.990690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.799096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:15.488875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.490028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:12.960549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:13.466049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.106706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:14.899175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:20:20.032759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호구간번호위치위치_방향표지종류표지명칭설치형식사진
식별번호1.0000.9710.9470.8970.2260.2490.3220.0001.000
관리번호0.9711.0000.9470.9390.0000.3550.3790.0001.000
구간번호0.9470.9471.0000.5890.0000.1030.539NaNNaN
위치0.8970.9390.5891.0000.1650.3550.3410.0001.000
위치_방향0.2260.0000.0000.1651.0000.0000.0440.0001.000
표지종류0.2490.3550.1030.3550.0001.0000.9940.8121.000
표지명칭0.3220.3790.5390.3410.0440.9941.0001.0000.943
설치형식0.0000.000NaN0.0000.0000.8121.0001.0001.000
사진1.0001.000NaN1.0001.0001.0000.9431.0001.000
2023-12-11T09:20:20.175040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구간번호위치_방향
구간번호1.0000.000
위치_방향0.0001.000
2023-12-11T09:20:20.590210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식별번호관리번호위치표지종류표지명칭설치형식구간번호위치_방향
식별번호1.0000.7400.1080.029-0.017-0.1340.9310.172
관리번호0.7401.0000.6800.114-0.0770.1990.9310.000
위치0.1080.6801.0000.154-0.1160.1980.4300.125
표지종류0.0290.1140.1541.0000.411-0.1190.1690.000
표지명칭-0.017-0.077-0.1160.4111.000-0.4050.2630.037
설치형식-0.1340.1990.198-0.119-0.4051.0001.0000.000
구간번호0.9310.9310.4300.1690.2631.0001.0000.000
위치_방향0.1720.0000.1250.0000.0370.0000.0001.000

Missing values

2023-12-11T09:20:15.631518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:20:15.849254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T09:20:15.972374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향표지종류표지명칭설치형식사진비고
0110200134168315041020103.295118074088<NA><NA>70km/h최고속도제한_(0)
1210200135168315041020103.295118054088<NA><NA>유턴_(좌,보행신호시)
2310200133168315041020103.294118054088<NA><NA>자전거횡단도_(0)
3410200132168315041020103.251118054088<NA><NA>좌측면통행_(0)
4510200002168315041020100.009018054088<NA><NA>자전거횡단도_(0)
5610200006168315041020100.023018074088<NA><NA>정차.주차금지_(0)
6710200007168315041020100.023018084088<NA><NA>보조표지 견인지역_(0)
7810200008168315041020100.037018054088<NA><NA>진행방향별통행구분_(0)
8910200009168315041020100.046018054088<NA><NA>자전거전용도로_(0)
91010200010168315041020100.073018074088<NA><NA>70km/h최고속도제한_(0)
식별번호관리번호관리기관도로종류노선번호구간번호이력코드위치위치_방향표지종류표지명칭설치형식사진비고
55956010200449168315041020401.8460189940991402102004M01846U연약지반 장기 압밀침하중
56056110200450168315041020402.310180740881403<NA>최고속도제한60
56156210200452168315041020401.8471180340101404102004M01847D2방향표지(수가리,율하리,장유면사무소)
56256310200451168315041020402.4780189940991401<NA>횡단버턴을 누르시오
56356410200453168315041020401.9781180740881403102004M01978D최고속도제한60
56456510200455168315041020402.311180340091404<NA>2방향예고표지(수가리,율하리,장유면사무소300M)
56556610200454168315041020403.810180740881401<NA>최고속도제한(60)
56656710200456168315041020402.4581180740881403102004M02458D최고속도제한60
56756810200457168315041020402.4781189940991401<NA>횡단버턴을 누르시오
56856910200458168315041020404.1950180740881401<NA>최고속도제한(60)